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Other titles in the Wiley Series in Probability & Mathematical Statistics series:
Introduction to Statistical Time Series (Wiley Series in Probability & Mathematical Statistics)by Wayne A Fuller
Synopses & Reviews
The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences. As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, and the Kalman filter.
Major topics include:
To accommodate a wide variety of readers, review material, especially on elementary results in Fourier analysis, large sample statistics, and difference equations, has been included.
Book News Annotation:
A textbook developed from a course in time series at Iowa State U., primarily for graduate students in economics and statistics. This edition retains the basic format of the first edition (1976), while incorporating new results and recent emphases in areas of activity including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. Prerequisites are an introductory graduate course in the theory of statistics and a course in linear regression analysis.
Annotation c. Book News, Inc., Portland, OR (booknews.com)
Includes bibliographical references (p. 664-688) and index.
About the Author
WAYNE A. FULLER is Distinguished Professor in the Departments of Statistics and Economics at Iowa State University. He is the author of Measurement Error Models and numerous articles in time series, survey sampling, and econometrics. A Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Econometric Society, he received his PhD in agricultural economics from Iowa State University.
Table of Contents
Moving Average and Autoregressive Processes.
Introduction to Fourier Analysis.
Spectral Theory and Filtering.
Some Large Sample Theory.
Estimation of the Mean and Autocorrelations.
The Periodogram, Estimated Spectrum.
Regression, Trend, and Seasonality.
Unit Root and Explosive Time Series.
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